A machine learning and/or deep learning frameworks involve various algorithms that define an initial model based on training data. The frameworks automatically adjust the initial model based on user feedback. Conventional frameworks cover a broad range of applications, such as organ detection, scan plane selection, segmentation and tissue classification. However, the conventional frameworks are isolated and specialized for specific applications. For example, each conventional framework is developed and trained for each application separately. During a scan and/or image analysis, one of the conventional frameworks are manually selected by the user utilizing a user interface. It would be desirable to have one machine learning and/or deep learning framework that includes all anatomical structures and applications. However, conventional frameworks would require training for all possible anatomical structures and/or user selections, which requires complex training and time to implement.